Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


In this paper, we consider the problem of predicting a large scale spatial field using successive noisy measurements obtained by mobile sensing agents. The physical spatial field of interest is discretized and modeled by a Gaussian Markov random field (GMRF) with unknown hyperparameters. From a Bayesian perspective, we design a sequential prediction algorithm to exactly compute the predictive inference of the random field. The prediction algorithm correctly takes into account the uncertainty in hyperparameters in a Bayesian way and also is scalable to be usable for the mobile sensor networks with limited resources. An adaptive sampling strategy is also designed for mobile sensing agents to find the most informative locations in taking future measurements in order to minimize the prediction error and the uncertainty in hyperparameters simultaneously. The effectiveness of the proposed algorithms is illustrated by a numerical experiment.

Original languageEnglish
Title of host publication2012 American Control Conference, ACC 2012
Number of pages6
Publication statusPublished - 2012
Event2012 American Control Conference, ACC 2012 - Montreal, QC, Canada
Duration: 2012 Jun 272012 Jun 29

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2012 American Control Conference, ACC 2012
CityMontreal, QC

Bibliographical note

Funding Information:
This work has been supported by the National Science Foundation through CAREER Award CMMI-0846547. This support is gratefully acknowledged. The material in this paper was partially presented at the 2012 American Control Conference (ACC12), June 27–29, 2012, Montreal, Canada. This paper was recommended for publication in revised form by Associate Editor Brett Ninness under the direction of Editor Torsten Söderström.

Funding Information:
Tapabrata Maiti is a world class statistician, a fellow of the American Statistical Association and the Institute of Mathematical Statistics. He has published research articles in top tier statistics journals such as the journal of the American Statistical Association, Annals of Statistics, the Journal of the Royal Statistical Society, Series B, Biometrika, Biometrics, etc. He has also published research articles in engineering, economics, genetics, medicine and social sciences. His research has been supported by the National Science Foundation and National Institutes of Health. He presented his work in numerous national and international meetings and in academic departments. Prof. Maiti served in editorial board of several statistics journals including journal of the American Statistical Association and journal of Agricultural, Environmental and Biological Statistics. He has also served on several professional committees. Currently, he is a professor and the graduate director in the department of statistics and probability, Michigan State University. Prior to MSU, he was a tenured faculty member in the department of statistics, Iowa State University. Professor Maiti has supervised several Ph.D. students and regularly teaches statistics and non-stat major graduate students.

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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